Discovery, Analysis, and Retrieval of Multimodal Environmental Information

Discovery, Analysis, and Retrieval of Multimodal Environmental Information

Anastasia Moumtzidou (Information Technologies Institute, Greece), Stefanos Vrochidis (Information Technologies Institute, Greece) and Ioannis Kompatsiaris (Information Technologies Institute, Centre for Research and Technology Hellas, Greece)
Copyright: © 2015 |Pages: 15
DOI: 10.4018/978-1-4666-5888-2.ch444
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Introduction

Environmental conditions are considered of utmost importance for human life. Citizens are increasingly aware of the important role that environmental data (i.e. weather forecast, air quality and pollen concentration) play on health issues (e.g. allergies), as well as to a variety of outdoor activities (e.g. agriculture, trip planning). Given the fact that ensembling information from several environmental providers can generate more reliable measurements, there is a need to combine environmental data from multiple resources, in order to facilitate retrieval of environmental information and support personalized services (Wanner et al., 2012).

In this context, this article analyzes the aforementioned needs and challenges (Figure 1) by discussing the application of techniques from the information technologies domain on environmental data. First, we address the discovery of environmental web resources (referred to as environmental nodes) as a domain-specific search problem. Then, we provide insights into the presentation formats of the environmental resources, as well as information extraction techniques that could be applied. Finally, we discuss indexing and retrieval of environmental information.

Figure 1.

Challenges in the environmental domain

The article is structured as follows. First we present the background and basic definitions regarding the environmental information. Then, an empirical study on the presentation of environmental data is realized. In the following sections, the approaches for environmental data discovery, content extraction, as well as indexing and retrieval are reported. Finally, we present future trends and conclusions.

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Empirical Analysis Of Environmental Resources

In order to discover and extract the environmental information available on the web, it is necessary to study the structure and the encoding format of the environmental resources.

Key Terms in this Chapter

Heatmap: A static image, which represents the coverage data as a color-coded scale over a geographical map and refers to specific time period.

Domain Specific Search: Search focusing on specific area of knowledge.

Information Extraction: Automatic extraction of structured information from unstructured or semi-structured machine-readable files.

Environmental Node: Web-sites or web services that provide environmental measurements.

Pollen: Power released by plants, which travels through air and may trigger allergic reactions to the people.

Indexing: The process of creating indices for large collections in order to facilitate faster retrieval.

Air Quality: The state of the air surrounding us. Good air quality equals to clean, unpolluted air compared to poor air quality.

Multimodal: It refers to multiple modalities that are considered in a procedure such as search or interaction. Examples of the first case are text, image, video, while the second includes speech, gaze, etc.

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